DEFAULT_PRECISION

precision

protected double precision

Precision, which is proportionate to the inverseRootFinder of variance, of the
distribution, must be greater than zero.
Note that we are using "precision" instead of "variance" because the
variance of the Student-t has another scale factor based on the dofs
and we want to avoid confusion.

mean

protected double mean

Mean, or noncentrality parameter, of the distribution

degreesOfFreedom

protected double degreesOfFreedom

Degrees of freedom in the distribution, usually the number of
datapoints - 1, DOFs must be greater than zero.

Constructor Detail

StudentTDistribution

public StudentTDistribution()

Default degrees of freedom.

StudentTDistribution

public StudentTDistribution(double degreesOfFreedom)

Creates a new instance of StudentTDistribution

Parameters:

degreesOfFreedom - Degrees of freedom in the distribution, usually the number of
datapoints - 1, DOFs must be greater than zero.

clone

This makes public the clone method on the Object class and
removes the exception that it throws. Its default behavior is to
automatically create a clone of the exact type of object that the
clone is called on and to copy all primitives but to keep all references,
which means it is a shallow copy.
Extensions of this class may want to override this method (but call
super.clone() to implement a "smart copy". That is, to target
the most common use case for creating a copy of the object. Because of
the default behavior being a shallow copy, extending classes only need
to handle fields that need to have a deeper copy (or those that need to
be reset). Some of the methods in ObjectUtil may be helpful in
implementing a custom clone method.
Note: The contract of this method is that you must use
super.clone() as the basis for your implementation.